Lightweight micro-motion gesture recognition based on MIMO millimeter wave radar using Bidirectional-GRU network

Zhao, Yaqin; Song, Yuqing; Wu, Longwen; Liu, Puqiu; Lv, Ruchen; Ullah, Hikmat

doi:10.1007/s00521-023-08978-z

Cited by 3 publications

(2 citation statements)

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“…Compared to Ref. [ 40 ]’s 8HBi-GRU model, the TRANS-CNN model uses point clouds as input data. When the quality of the gesture trajectory is not as strong as the micro-Doppler image, the gesture recognition accuracy is improved by 0.3%, and gesture recognition can be completed within 1 s. Compared to the DenseNet-CBAM model designed in Ref.…”

Section: Resultsmentioning

confidence: 99%

TRANS-CNN-Based Gesture Recognition for mmWave Radar

Zhang,

Liu,

Zhang

et al. 2024

Sensors

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In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.

show abstract

Section: Resultsmentioning

confidence: 99%

TRANS-CNN-Based Gesture Recognition for mmWave Radar

Zhang,

Liu,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Compared to Ref. [40]'s 8HBi-GRU model, the TRANS-CNN model uses point clouds as input data. When the quality of the gesture is not as strong as the micro-Doppler image, the gesture recognition accuracy is improved by 0.3%, and gesture recognition can be completed within 1 s. Compared to the DenseNet-CBAM model designed in Ref.…”

Section: Recognition Results and Evaluationmentioning

confidence: 99%

TRANS-CNN Based Gesture Recognition for mmWave Radar

Zhang,

Liu,

Zhang

2024

Preprint

View full text Add to dashboard Cite

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

show abstract